In short Don’t use softmax. Use sigmoid for activation of your output layer. Use binary_crossentropy for loss function. Use predict for evaluation. Why In softmax when increasing score for one label, all others are lowered (it’s a probability distribution). You don’t want that when you have multiple labels. Complete Code from tensorflow.keras.models import Sequential from … Read more
You need to add return_sequences=True to the first layer so that its output tensor has ndim=3 (i.e. batch size, timesteps, hidden state). Please see the following example: # expected input data shape: (batch_size, timesteps, data_dim) model = Sequential() model.add(LSTM(32, return_sequences=True, input_shape=(timesteps, data_dim))) # returns a sequence of vectors of dimension 32 model.add(LSTM(32, return_sequences=True)) # returns … Read more
I found the answer. I looked into Keras sources and find out code for EarlyStopping. I made my own callback, based on it: class EarlyStoppingByLossVal(Callback): def __init__(self, monitor=”val_loss”, value=0.00001, verbose=0): super(Callback, self).__init__() self.monitor = monitor self.value = value self.verbose = verbose def on_epoch_end(self, epoch, logs={}): current = logs.get(self.monitor) if current is None: warnings.warn(“Early stopping requires … Read more
For me the solution was downgrading the h5py package (in my case to 2.10.0), apparently putting back only Keras and Tensorflow to the correct versions was not enough.
Python library authors put the version number in <module>.__version__. You can print it by running this on the command line: python -c ‘import keras; print(keras.__version__)’ If it’s Windows terminal, enclose snippet with double-quotes like below python -c “import keras; print(keras.__version__)”
Simply: categorical_crossentropy (cce) produces a one-hot array containing the probable match for each category, sparse_categorical_crossentropy (scce) produces a category index of the most likely matching category. Consider a classification problem with 5 categories (or classes). In the case of cce, the one-hot target may be [0, 1, 0, 0, 0] and the model may predict … Read more
LSTM in its core, preserves information from inputs that has already passed through it using the hidden state. Unidirectional LSTM only preserves information of the past because the only inputs it has seen are from the past. Using bidirectional will run your inputs in two ways, one from past to future and one from future … Read more
This means that the index that will be returned by argmax will be taken from the last axis. Your data has some shape (19,19,5,80). This means: Axis 0 = 19 elements Axis 1 = 19 elements Axis 2 = 5 elements Axis 3 = 80 elements Now, negative numbers work exactly like in python lists, … Read more
I suggest taking a look at (the first part of) this paper. Regular dropout is applied on the inputs and/or the outputs, meaning the vertical arrows from x_t and to h_t. In your case, if you add it as an argument to your layer, it will mask the inputs; you can add a Dropout layer … Read more
If you want to build a solid model you have to follow that specific protocol of splitting your data into three sets: One for training, one for validation and one for final evaluation, which is the test set. The idea is that you train on your training data and tune your model with the results … Read more